forecast_with_predictors

class Optimization:
# ...

    def forecast_with_predictors(
        self, forecast_loader, batch_size=1, n_features=1, n_steps=100
    ):
        """Forecasts values for RNNs with predictors and one-dimensional output

        The method takes DataLoader for the test dataset, batch size for mini-batch testing,
        number of features and number of steps to predict as inputs. Then it generates the
        future values for RNNs with one-dimensional output for the given n_steps. It uses the
        values from the predictors columns (features) to forecast the future values.

        Args:
            forecast_loader (torch.utils.data.DataLoader): DataLoader that stores test data
            batch_size (int): Batch size for mini-batch training
            n_features (int): Number of feature columns
            n_steps (int): Number of steps to predict future values

        Returns:
            list[float]: The values predicted by the model

        """
        step = 0
        with torch.no_grad():
            predictions = []
            for x_test, _ in forecast_loader:
                x_test = x_test.view([batch_size, -1, n_features]).to(device)
                self.model.eval()
                yhat = self.model(x_test)
                predictions.append(yhat.to(device).detach().numpy())

                step += 1
                if step == n_steps:
                    break

        return predictions   
# ...